SPREAD: Scalable Pre-Trained World Model for Adaptive Dynamics Model

Abstract

Autonomous robots must be capable of adapting not only to training datasets but also to unfamiliar environments. World models, which learn predictive dynamics of the environment, have been proposed to overcome the task-specific limitations of conventional RL. However, their capability is typically restricted to training distributions. In this paper, we introduce SPREAD, the first framework that enables a pre-trained world model to learn environmental changes online, and continuously. SPREAD freezes the pre-trained model and updates only a set of LoRA adapters. This design jointly achieves three goals: (i) real-time adaptation, (ii) robustness to forgetting without a replay buffer, and (iii) forward transfer. Experiments demonstrate that SPREAD reduces prediction error by 40% within just 100 steps under diverse visual and dynamic changes, and that such adaptation directly translates into improved task success rates. Moreover, SPREAD validates its effectiveness in continual learning settings. By extending pre-trained world models to the domain of online continual learning, this work highlights world models that autonomously adapt to real-world dynamics.